OpenRaiser/Intern-Atlas huggingface dataset:
Terahertz Wireless Transmissions with Maximal Ratio Combining over Fluctuating Two-Ray Fading
Venue: arXiv cs.IT 2021
Year: 2021

Mitigating channel fading and transceiver impairments are desirable for high-speed terahertz (THz) wireless links. This paper analyzes the performance of a multi-antenna THz wireless system by considering the combined effect of pointing errors and fluctuating two-ray (FTR) fading model. We provide a statistical characterization of the maximal ratio combining (MRC) receiver over independent and nonidentical (i.ni.d.) channel conditions in terms of multi-variate Fox's H by deriving density and distribution functions of the signal-to-noise ratio (SNR) of a single-link THz link using incomplete Gamma function. We develop exact analytical expressions of outage probability, average bit-error-rate (BER), and ergodic capacity for both single-antenna and MRC receivers. We also present the diversity order of the system by deriving asymptotic expressions for outage probability and average BER at high SNR to obtain insights into the system performance. We validate our derived analytical expressions with Monte-Carlo simulations and demonstrate the effect of various system and channel parameters on the performance of single and multi-antenna THz wireless communications.
Optimally Hiding Object Sizes with Constrained Padding
Venue: arXiv cs.CR 2021
Year: 2021

Among the most challenging traffic-analysis attacks to confound are those leveraging the sizes of objects downloaded over the network. In this paper we systematically analyze this problem under realistic constraints regarding the padding overhead that the object store is willing to incur. We give algorithms to compute privacy-optimal padding schemes -- specifically that minimize the network observer's information gain from a downloaded object's padded size -- in several scenarios of interest: per-object padding, in which the object store responds to each request for an object with the same padded copy; per-request padding, in which the object store pads an object anew each time it serves that object; and a scenario unlike the previous ones in that the object store is unable to leverage a known distribution over the object queries. We provide constructions for privacy-optimal padding in each case, compare them to recent contenders in the research literature, and evaluate their performance on practical datasets.
Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
Venue: arXiv cs.NE 2021
Year: 2021

Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network dynamics, even when the weight resolution is limited. These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing to resolve the von-Neumann bottleneck problem, at the expense of a substantial increase in variability in both the computation and the working memory of the spiking RNNs. To address these challenges and enable online learning in memristive neuromorphic RNNs, we present a simulation framework of differential-architecture crossbar arrays based on an accurate and comprehensive Phase-Change Memory (PCM) device model. We train a spiking RNN whose weights are emulated in the presented simulation framework, using a recently proposed e-prop learning rule. Although e-prop locally approximates the ideal synaptic updates, it is difficult to implement the updates on the memristive substrate due to substantial PCM non-idealities. We compare several widely adapted weight update schemes that primarily aim to cope with these device non-idealities and demonstrate that accumulating gradients can enable online and efficient training of spiking RNN on memristive substrates.
Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text
Venue: arXiv cs.CL 2021
Year: 2021

In this shared task, we seek the participating teams to investigate the factors influencing the quality of the code-mixed text generation systems. We synthetically generate code-mixed Hinglish sentences using two distinct approaches and employ human annotators to rate the generation quality. We propose two subtasks, quality rating prediction and annotators' disagreement prediction of the synthetic Hinglish dataset. The proposed subtasks will put forward the reasoning and explanation of the factors influencing the quality and human perception of the code-mixed text.
Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for terrain monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. However, a key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. To address this, we propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas on the terrain with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on the application of crop/weed segmentation in precision agriculture using real-world field data.
PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining
Venue: arXiv cs.CL 2021
Year: 2021

Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora, and do not make use of the strong cross-lingual signal contained in parallel data. In this paper, we present PARADISE (PARAllel & Denoising Integration in SEquence-to-sequence models), which extends the conventional denoising objective used to train these models by (i) replacing words in the noised sequence according to a multilingual dictionary, and (ii) predicting the reference translation according to a parallel corpus instead of recovering the original sequence. Our experiments on machine translation and cross-lingual natural language inference show an average improvement of 2.0 BLEU points and 6.7 accuracy points from integrating parallel data into pretraining, respectively, obtaining results that are competitive with several popular models at a fraction of their computational cost.
An analytical study of content and contexts of keywords on physics
Venue: arXiv cs.IR 2021
Year: 2021

This paper analysed author-assigned and title keywords into constituent words collected from 769 articles published in the journal Low Temperature Physics since the year 2006 to 2010. The total number of distinct keywords over the said time span has been found as 1155, which have been analyzed into 869 numbers of single words having total frequency of occurrence of 2287. The single words obtained from keywords have been categorized in four broad classes, viz. eponymous word, form word, acronym and semantic word. A semantic word bears several contexts and thus may be considered as relevant in several other subject areas. These probable relevant subject areas have been found with the aid of two popular online reference tools. The semantic words are further categorized in twelve classes according to their contexts. Some parameters have been defined on the basis of associations among the words and formation of keywords in consequence, i.e. Word Association Density, Word Association Coefficient and Keyword Formation Density. The values of these parameters have been observed for different word categories. The statistics of word association tending keyword formation would be known from this study. The allied subject domains also become predictable from this study.
TextCNN with Attention for Text Classification
Venue: arXiv cs.CL 2021
Year: 2021

The vast majority of textual content is unstructured, making automated classification an important task for many applications. The goal of text classification is to automatically classify text documents into one or more predefined categories. Recently proposed simple architectures for text classification such as Convolutional Neural Networks for Sentence Classification by Kim, Yoon showed promising results. In this paper, we propose incorporating an attention mechanism into the network to boost its performance, we also propose WordRank for vocabulary selection to reduce the network embedding parameters and speed up training with minimum accuracy loss. By adopting the proposed ideas TextCNN accuracy on 20News increased from 94.79 to 96.88, moreover, the number of parameters for the embedding layer can be reduced substantially with little accuracy loss by using WordRank. By using WordRank for vocabulary selection we can reduce the number of parameters by more than 5x from 7.9M to 1.5M, and the accuracy will only decrease by 1.2%.
Using Interaction Data to Predict Engagement with Interactive Media
Venue: arXiv cs.HC 2021
Year: 2021

Media is evolving from traditional linear narratives to personalised experiences, where control over information (or how it is presented) is given to individual audience members. Measuring and understanding audience engagement with this media is important in at least two ways: (1) a post-hoc understanding of how engaged audiences are with the content will help production teams learn from experience and improve future productions; (2), this type of media has potential for real-time measures of engagement to be used to enhance the user experience by adapting content on-the-fly. Engagement is typically measured by asking samples of users to self-report, which is time consuming and expensive. In some domains, however, interaction data have been used to infer engagement. Fortuitously, the nature of interactive media facilitates a much richer set of interaction data than traditional media; our research aims to understand if these data can be used to infer audience engagement. In this paper, we report a study using data captured from audience interactions with an interactive TV show to model and predict engagement. We find that temporal metrics, including overall time spent on the experience and the interval between events, are predictive of engagement. The results demonstrate that interaction data can be used to infer users' engagement during and after an experience, and the proposed techniques are relevant to better understand audience preference and responses.
Log-based Anomaly Detection Without Log Parsing
Venue: arXiv cs.SE 2021
Year: 2021

Software systems often record important runtime information in system logs for troubleshooting purposes. There have been many studies that use log data to construct machine learning models for detecting system anomalies. Through our empirical study, we find that existing log-based anomaly detection approaches are significantly affected by log parsing errors that are introduced by 1) OOV (out-of-vocabulary) words, and 2) semantic misunderstandings. The log parsing errors could cause the loss of important information for anomaly detection. To address the limitations of existing methods, we propose NeuralLog, a novel log-based anomaly detection approach that does not require log parsing. NeuralLog extracts the semantic meaning of raw log messages and represents them as semantic vectors. These representation vectors are then used to detect anomalies through a Transformer-based classification model, which can capture the contextual information from log sequences. Our experimental results show that the proposed approach can effectively understand the semantic meaning of log messages and achieve accurate anomaly detection results. Overall, NeuralLog achieves F1-scores greater than 0.95 on four public datasets, outperforming the existing approaches.
Learning Compatible Embeddings
Venue: arXiv cs.CV 2021
Year: 2021

Achieving backward compatibility when rolling out new models can highly reduce costs or even bypass feature re-encoding of existing gallery images for in-production visual retrieval systems. Previous related works usually leverage losses used in knowledge distillation which can cause performance degradations or not guarantee compatibility. To address these issues, we propose a general framework called Learning Compatible Embeddings (LCE) which is applicable for both cross model compatibility and compatible training in direct/forward/backward manners. Our compatibility is achieved by aligning class centers between models directly or via a transformation, and restricting more compact intra-class distributions for the new model. Experiments are conducted in extensive scenarios such as changes of training dataset, loss functions, network architectures as well as feature dimensions, and demonstrate that LCE efficiently enables model compatibility with marginal sacrifices of accuracies. The code will be available at https://github.com/IrvingMeng/LCE.
From Textual to Verbal Communication: Towards Applying Sentiment Analysis to a Software Project Meeting
Venue: arXiv cs.SE 2021
Year: 2021

Sentiment analysis gets increasing attention in software engineering with new tools emerging from new insights provided by researchers. Existing use cases and tools are meant to be used for textual communication such as comments on collaborative version control systems. While this can already provide useful feedback for development teams, a lot of communication takes place in meetings and is not suited for present tool designs and concepts. In this paper, we present a concept that is capable of processing live meeting audio and classifying transcribed statements into sentiment polarity classes. We combine the latest advances in open source speech recognition with previous research in sentiment analysis. We tested our approach on a student software project meeting to gain proof of concept, showing moderate agreement between the classifications of our tool and a human observer on the meeting audio. Despite the preliminary character of our study, we see promising results motivating future research in sentiment analysis on meetings. For example, the polarity classification can be extended to detect destructive behaviour that can endanger project success.
On Extending Brandt's Speedup Theorem from LOCAL to Round-Based Full-Information Models
Venue: arXiv cs.DC 2021
Year: 2021

Given any task $Π$, Brandt's speedup theorem (PODC 2019) provides a mechanical way to design another task~$Π'$ on the same input-set as $Π$ such that, for any $t\geq 1$, $Π$ is solvable in $t$ rounds if and only if $Π'$ is solvable in $t-1$ rounds. The theorem applies to the anonymous variant of the LOCAL model, in graphs with sufficiently large girth, and to locally checkable labeling (LCL) tasks. In this paper, using combinatorial topology applied to distributed computing, we dissect the construction in Brandt's speedup theorem for expressing it in the broader framework of round-based models supporting full information protocols, which includes models as different as wait-free shared-memory computing with iterated immediate snapshots, and synchronous failure-free network computing. In particular, we provide general definitions for notions such as local checkability and local independence, in our broader framework. In this way, we are able to identify the hypotheses on the computing model, and on the tasks, that are sufficient for Brandt's speedup theorem to apply. More precisely, we identify which hypotheses are sufficient for the each direction of the if-and-only-if condition. Interestingly, these hypotheses are of different natures. Our general approach enables to extend Brandt's speedup theorem from LOCAL to directed networks, to hypergraphs, to dynamic networks, and even to graphs including short cyclic dependencies between processes (i.e., the large girth condition is, to some extend, not necessary). The theorem can even be extended to shared-memory wait-free computing. In particular, we provide new impossibility proofs for consensus and perfect renaming in 2-process systems.
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Venue: arXiv cs.CV 2021
Year: 2021

Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in multi-label learning is extremely expensive, but also many of the actual labels are corrupted or missing due to the automated or non-expert annotation techniques. Noisy label data decrease the prediction performance drastically. In this paper, we propose a novel Gold Asymmetric Loss Correction with Single-Label Regulators (GALC-SLR) that operates robust against noisy labels. GALC-SLR estimates the noise confusion matrix using single-label samples, then constructs an asymmetric loss correction via estimated confusion matrix to avoid overfitting to the noisy labels. Empirical results show that our method outperforms the state-of-the-art original asymmetric loss multi-label classifier under all corruption levels, showing mean average precision improvement up to 28.67% on a real world dataset of MS-COCO, yielding a better generalization of the unseen data and increased prediction performance.
Convergence of gradient descent for learning linear neural networks
Venue: arXiv cs.LG 2021
Year: 2021

We study the convergence properties of gradient descent for training deep linear neural networks, i.e., deep matrix factorizations, by extending a previous analysis for the related gradient flow. We show that under suitable conditions on the step sizes gradient descent converges to a critical point of the loss function, i.e., the square loss in this article. Furthermore, we demonstrate that for almost all initializations gradient descent converges to a global minimum in the case of two layers. In the case of three or more layers we show that gradient descent converges to a global minimum on the manifold matrices of some fixed rank, where the rank cannot be determined a priori.
Real-World Trajectory Sharing with Local Differential Privacy
Venue: arXiv cs.DB 2021
Year: 2021

Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping $n$-grams (i.e., contiguous subsequences of length $n$) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories. Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.
Randomized Local Fast Rerouting for Datacenter Networks with Almost Optimal Congestion
Venue: arXiv cs.DC 2021
Year: 2021

To ensure high availability, datacenter networks must rely on local fast rerouting mechanisms that allow routers to quickly react to link failures, in a fully decentralized manner. However, configuring these mechanisms to provide a high resilience against multiple failures while avoiding congestion along failover routes is algorithmically challenging, as the rerouting rules can only depend on local failure information and must be defined ahead of time. This paper presents a randomized local fast rerouting algorithm for Clos networks, the predominant datacenter topologies. Given a graph $G=(V,E)$ describing a Clos topology, our algorithm defines local routing rules for each node $v\in V$, which only depend on the packet's destination and are conditioned on the incident link failures. We prove that as long as number of failures at each node does not exceed a certain bound, our algorithm achieves an asymptotically minimal congestion up to polyloglog factors along failover paths. Our lower bounds are developed under some natural routing assumptions.
On the Potential of Zines as a Medium for Visualization
Venue: arXiv cs.HC 2021
Year: 2021

Zines are a form of small-circulation self-produced publication often akin to a magazine. This free-form medium has a long history and has been used as means for personal or intimate expression, as a way for marginalized people to describe issues that are important to them, and as a venue for graphical experimentation. It would seem then that zines would make an ideal vehicle for the recent interest in applying feminist or humanist ideas to visualization. Yet, there has been little work combining visualization and zines. In this paper we explore the potential of this intersection by analyzing examples of zines that use data graphics and by describing the pedagogical value that they can have in a visualization classroom. In doing so, we argue that there are plentiful opportunities for visualization research and practice in this rich intersectional-medium.
A Biologically Plausible Parser
Venue: arXiv cs.CL 2021
Year: 2021

We describe a parser of English effectuated by biologically plausible neurons and synapses, and implemented through the Assembly Calculus, a recently proposed computational framework for cognitive function. We demonstrate that this device is capable of correctly parsing reasonably nontrivial sentences. While our experiments entail rather simple sentences in English, our results suggest that the parser can be extended beyond what we have implemented, to several directions encompassing much of language. For example, we present a simple Russian version of the parser, and discuss how to handle recursion, embedding, and polysemy.